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This information can be used to:
  • build new LLM judges
  • form the basis for new datasets
  • help identify ideas for improving your application

​
Pulling Spans

from phoenix.client import Client

client = Client()

spans = client.spans.get_spans_dataframe(
    project_identifier="default",  # you can also pass a project id
)
If you only want the spans that contain a specific annotation, you can pass in a query that filters on annotation names, scores, or labels. 
from phoenix.client import Client
from phoenix.client.types.span import SpanQuery

client = Client()
query = SpanQuery().where("annotations['correctness']")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)
The queries can also filter by annotation scores and labels. 
from phoenix.client import Client
from phoenix.client.types.span import SpanQuery

client = Client()
query = SpanQuery().where("annotations['correctness'].score == 1")
# query = SpanQuery().where("annotations['correctness'].label == 'correct'")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)
This spans dataframe can be used to pull associated annotations. 
annotations = client.spans.get_span_annotations_dataframe(
    spans_dataframe=spans,
    project_identifier="default",
)
Instead of an input dataframe, you can also pass in a list of ids: 
annotations = client.spans.get_span_annotations_dataframe(
    span_ids=list[spans.index],
    project_identifier="default",
)
The annotations and spans dataframes can be easily joined to produce a one-row-per-annotation dataframe that can be used to analyze the annotations! 
annotations.join(spans, how="left")